Model To Be Studied By Residual 1. The regression function is not linear. 2. The error terms do not have constant variance. 3. The error terms are not independent. 4. The model fits all but one or few outliers‚ 5. The error terms are not normally distributed. 6. One or several important predictor(s) have been omitted from the model. Diagnostic For Residuals Six diagnostic plots to judge departure from the simple linear regression model * Plot of residuals against predictor
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Regression with a Binary Dependent Variable Binary Dependent Variables and the Linear Probability Model • • • Many of the decisions made by people are binary. What factors drive a person’s decision? This question leads to regression with a binary dependent variable. The binary choice problem is an example of models with limited dependent variables (see Appendix 9.3 for details). Note that the multiple regression model discussed earlier does not preclude a dependent variable from being binary
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08 ETHE AUSTRALIAN NATIONAL UNIVERSITY SCHOOL OF FINANCE AND APPLIED STATISTICS First Semester Examination 2010 QUANTITATIVE RESEARCH METHODS (STAT1008) Writing Period: 3 hours duration Study Period: 15 minutes duration Permitted Material: Non-programmable calculator‚ dictionary and 1 A4 page with notes on both sides Instructions to Candidates: • Attempt ALL questions. • Each question is of equal mark value. • Start your solution to each question on a new page. • To ensure full marks
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time flown are correlated so between these cost drivers‚ available ton miles seems to be the most reasonable cost driver since it indicate the time that the pilots and the flight attendant work for the Delta. Question 2 We first apply simple regression using each of the cost drivers mention above and other factor to estimate the salary by the cost drivers individually to see which one is best cost driver based on statistical reason and comparing R square. The scatter plots are shown in appendix
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the points drag the trend line and if there are outliers. I have found one possible outlier (in red). I need to run a multiple regression with and without the possible outlier. If there is an important change in the output‚ I can consider to deleting the outlier but it is always important to think about some reasons why I need to delete the outlier. Regression MSHARE = 4.0303 - 7.5977 * PDUB + 2.6223 * PMAY + 3.4727 * PBPREG + 1.0249 * PBPALL Without the outlier MSHARE = 4.2352 - 6.9540
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Credits 3 Prerequisites EPSE 482 and EPSE 481 Instructor Dr. Amery Wu Course Correspondence email at amery.wu@ubc.ca Office Hours By appointment via email Textbook Cohen‚ J.‚ Cohen‚ P.‚ & Stephen‚ G. West‚ and Leona S. Aiken (2003). Applied multiple regression/correlation analysis for the behavioral sciences (Third Edition) ISBN: 978-0805822236 Other Support The Department of ECPS provides methodology support to its students who are taking quantitative research-related courses or who need quantitative
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Regression Analysis: IBI versus Area The regression equation is IBI = 52.9 + 0.460 Area Predictor Coef SE Coef T P Constant 52.923 4.484 11.80 0.000 Area 0.4602 0.1347 3.42 0.001 S = 16.5346 R-Sq = 19.9% R-Sq(adj) = 18.2% Analysis of Variance Source DF SS MS F P Regression 1 3189.3 3189.3 11.67 0.001 Residual Error 47 12849.5 273.4 Total 48 16038.8 Unusual Observations Obs Area
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Crime Rates: An Econometric Analysis using population‚ unemployment and growth Table of Contents I. Introduction A.) Background of the Study B.) Problem Statement C.) Objectives D.) Significance of the Study E.) Scope and Limitations II. Review of Related Literature III. Operational Framework A.) Variable List B.) Model Specification C.) A-priori Expectations IV. Methodology A.) Data B.) Preliminary Tests V. Results and Discussions VI. Conclusion
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following questions please give a True or False answer with one or two sentences in justification. 1.1 A linear regression model will be developed using a training data set. Adding variables to the model will always reduce the sum of squared residuals measured on the validation set. 1.2 Although forward selection and backward elimination are fast methods for subset selection in linear regression‚ only step-wise selection is guaranteed to find the best subset. 1.3 An analyst computes classification functions
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Question No. 1 A survey to collect data on the entire population is a census a sample a population an inference Question No. 2 A portion of the population selected to represent the population is called statistical inference descriptive statistics a census a sample Question No. 3 Qualitative data can be graphically represented by using a(n) Options histogram frequency polygon ogive bar graph Question No. 4 Fifteen percent of the students in a school of Business Administration
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